Provided by: cnvkit_0.9.12-1_all 

NAME
cnvkit_segmetrics - Compute segment-level metrics from bin-level log2 ratios.
DESCRIPTION
usage: cnvkit.py segmetrics [-h] -s SEGMENTS [--drop-low-coverage]
[-o FILENAME] [--mean] [--median] [--mode]
[--t-test] [--stdev] [--sem] [--mad] [--mse] [--iqr] [--bivar] [--ci] [--pi] [-a ALPHA] [-b
BOOTSTRAP] [--smooth-bootstrap] cnarray
positional arguments:
cnarray
Bin-level copy ratio data file (*.cnn, *.cnr).
options:
-h, --help
show this help message and exit
-s SEGMENTS, --segments SEGMENTS
Segmentation data file (*.cns, output of the 'segment' command).
--drop-low-coverage
Drop very-low-coverage bins before calculations to avoid negative bias in poor-quality tumor
samples.
-o FILENAME, --output FILENAME
Output table file name.
Statistics available:
--mean Mean log2 ratio (unweighted).
--median
Median.
--mode Mode (i.e. peak density of bin log2 ratios).
--t-test
One-sample t-test of bin log2 ratios versus 0.0.
--stdev
Standard deviation.
--sem Standard error of the mean.
--mad Median absolute deviation (standardized).
--mse Mean squared error.
--iqr Inter-quartile range.
--bivar
Tukey's biweight midvariance.
--ci Confidence interval (by bootstrap).
--pi Prediction interval.
-a ALPHA, --alpha ALPHA
Level to estimate confidence and prediction intervals; use with --ci and --pi. [Default: 0.05]
-b BOOTSTRAP, --bootstrap BOOTSTRAP
Number of bootstrap iterations to estimate confidence interval; use with --ci. [Default: 100]
--smooth-bootstrap
Apply Gaussian noise to bootstrap samples, a.k.a. smoothed bootstrap, to estimate confidence
interval; use with --ci.
cnvkit.py segmetrics 0.9.10 July 2023 CNVKIT_SEGMETRICS(1)